Single Field Of View Research Articles

Strength in numbers: Unleashing the potential of trans-scale scope AMATERAS for massive cell quantification.

Singularity biology is a scientific field that targets drastic state changes in multicellular systems, aiming to discover the key cells that induce the state change and investigate the mechanisms behind them. To achieve this goal, we developed a trans-scale optical imaging system (trans-scale scope), that is capable of capturing both macroscale changes across the entire system and the micro-scale behavior of individual cells, surpassing the cell observation capabilities of traditional microscopes. We developed two units of the trans-scale scope, named AMATERAS-1 and -2, which demonstrated the ability to observe multicellular systems consisting of over one million cells in a single field of view with sub-cellular resolution. This flagship instrument has been used to observe the dynamics of various cell species, with the advantage of being able to observe a large number of cells, allowing the detection and analysis of rare events and cells such as leader cells in multicellular pattern formation and cells that spontaneously initiate calcium waves. In this paper, we present the design concept of AMATERAS, the optical configuration, and several examples of observations, and demonstrate how the strength-in-numbers works in life sciences.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

Multiplexed and Millimeter-Scale Fluorescence Nanoscopy of Cells and Tissue Sections via Prism-Illumination and Microfluidics-Enhanced DNA-PAINT.

Fluorescence nanoscopy has become increasingly powerful for biomedical research, but it has historically afforded a small field-of-view (FOV) of around 50 μm × 50 μm at once and more recently up to ∼200 μm × 200 μm. Efforts to further increase the FOV in fluorescence nanoscopy have thus far relied on the use of fabricated waveguide substrates, adding cost and sample constraints to the applications. Here we report PRism-Illumination and Microfluidics-Enhanced DNA-PAINT (PRIME-PAINT) for multiplexed fluorescence nanoscopy across millimeter-scale FOVs. Built upon the well-established prism-type total internal reflection microscopy, PRIME-PAINT achieves robust single-molecule localization with up to ∼520 μm × 520 μm single FOVs and 25-40 nm lateral resolutions. Through stitching, nanoscopic imaging over mm2 sample areas can be completed in as little as 40 min per target. An on-stage microfluidics chamber facilitates probe exchange for multiplexing and enhances image quality, particularly for formalin-fixed paraffin-embedded (FFPE) tissue sections. We demonstrate the utility of PRIME-PAINT by analyzing ∼106 caveolae structures in ∼1,000 cells and imaging entire pancreatic cancer lesions from patient tissue biopsies. By imaging from nanometers to millimeters with multiplexity and broad sample compatibility, PRIME-PAINT will be useful for building multiscale, Google-Earth-like views of biological systems.

MVCINN: Multi-View Diabetic Retinopathy Detection Using a Deep Cross-Interaction Neural Network

Diabetic retinopathy (DR) is the main cause of irreversible blindness for working-age adults. The previous models for DR detection have difficulties in clinical application. The main reason is that most of the previous methods only use single-view data, and the single field of view (FOV) only accounts for about 13% of the FOV of the retina, resulting in the loss of most lesion features. To alleviate this problem, we propose a multi-view model for DR detection, which takes full advantage of multi-view images covering almost all of the retinal field. To be specific, we design a Cross-Interaction Self-Attention based Module (CISAM) that interfuses local features extracted from convolutional blocks with long-range global features learned from transformer blocks. Furthermore, considering the pathological association in different views, we use the feature jigsaw to assemble and learn the features of multiple views. Extensive experiments on the latest public multi-view MFIDDR dataset with 34,452 images demonstrate the superiority of our method, which performs favorably against state-of-the-art models. To the best of our knowledge, this work is the first study on the public large-scale multi-view fundus images dataset for DR detection.

Transmission structured illumination microscopy with tunable frequency illumination using tilt mirror assembly

We present experimental demonstration of tilt-mirror assisted transmission structured illumination microscopy (tSIM) that offers a large field of view super resolution imaging. An assembly of custom-designed tilt-mirrors are employed as the illumination module where the sample is excited with the interference of two beams reflected from the opposite pair of mirror facets. Tunable frequency structured patterns are generated by changing the mirror-tilt angle and the hexagonal-symmetric arrangement is considered for the isotropic resolution in three orientations. Utilizing high numerical aperture (NA) objective in standard SIM provides super-resolution compromising with the field-of-view (FOV). Employing low NA (20X/0.4) objective lens detection, we experimentally demonstrate sim (0.56 mmtimes0.35 mm) size single FOV image with sim1.7- and sim2.4-fold resolution improvement (exploiting various illumination by tuning tilt-mirrors) over the diffraction limit. The results are verified both for the fluorescent beads as well as biological samples. The tSIM geometry decouples the illumination and the collection light paths consequently enabling free change of the imaging objective lens without influencing the spatial frequency of the illumination pattern that are defined by the tilt-mirrors. The large and scalable FOV supported by tSIM will find usage for applications where scanning large areas are necessary as in pathology and applications where images must be correlated both in space and time.

Imaging and plate counting to quantify the effect of an antimicrobial: A case study of a photo-activated chlorine dioxide treatment.

To assess removal versus kill efficacies of antimicrobial treatments against thick biofilms with statistical confidence. A photo-activated chlorine dioxide treatment (Photo ClO2 ) was tested in two independent experiments against thick (>100 μm) Pseudomonas aeruginosa biofilms. Kill efficacy was assessed by viable plate counts. Removal efficacy was assessed by 3D confocal scanning laser microscope imaging (CSLM). Biovolumes were calculated using an image analysis approach that models the penetration limitation of the laser into thick biofilms using Beer's Law. Error bars are provided that account for the spatial correlation of the biofilm's surface. The responsiveness of the biovolumes and plate counts to the increasing contact time of Photo ClO2 were quite different, with a massive 7 log reduction in viable cells (95% confidence interval [CI]: 6.2, 7.9) but a more moderate 73% reduction in biovolume (95% CI: [60%, 100%]). Results are leveraged to quantitatively assess candidate CSLM experimental designs of thick biofilms. Photo ClO2 kills biofilm bacteria but only partially removes the biofilm from the surface. To maximize statistical confidence in assessing removal, imaging experiments should use fewer pixels in each z-slice, and more importantly, at least two independent experiments even if there is only a single field of view in each experiment. There is limited penetration depth when collecting 3D confocal images of thick biofilms. Removal can be assessed by optimally fitting Beer's Law to all of the intensities in a 3D image and by accounting for the spatial correlation of the biofilm's surface. For thick biofilms, other image analysis approaches are biased or do not provide error bars. We generate unbiased estimates of removal and assess candidate CSLM experimental designs of thick biofilms with different pixilations, numbers of fields of view and number of experiments using the included design tool.

Simultaneous Optical Detection of Multiple Bacterial Species Using Nanometer-Scaled Metal-Organic Hybrids.

This paper describes a simple strategy to identify bacteria using the optical properties of the nanohybrid structures (NHs) of polymer-coated metal nanoparticles (NPs). NHs, in which many small NPs are encapsulated in polyaniline particles, are useful optical labels because they produce strong scattered light. The light-scattering characteristics of NHs are strongly dependent on the constituent metal elements of NPs. Gold NHs (AuNHs), silver NHs (AgNHs), and copper NHs (CuNHs) produce white, reddish, and bluish scattered light, respectively. Moreover, unlike NPs, the color of the scattered light does not change even when NHs are aggregated. Introducing an antibody into NHs induces antigen-specific binding to cells, enabling the identification of bacteria based on light scattering. Multiple bacterial species adsorbed on the slide can be identified within a single field of view under a dark field microscope based on the color of the scattered light. Therefore, it is a useful development for safety risk assessments at manufacturing sites, such as those for foods, beverages, and drugs, and environmental surveys that require rapid detection of multiple bacteria.

A Neural Network Based Single Footprint Temperature Retrieval for Atmospheric Infrared Sounder Measurements and Its Application to Study on Stratospheric Gravity Wave

Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave (GW) analysis. To accurately and efficiently derive the three- dimensional structure of the stratospheric GWs from the single-field-of-view (SFOV) Atmospheric InfraRed Sounder (AIRS) observations, this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach (ANN). The simulation experiments show that the retrieval bias is less than 0.5 K, and the root mean square error (RMSE) ranges from 1.8 to 4 K. Moreover, the retrieval results from 20 granules of the AIRS observations with the trained neural network (AIRS_SFOV) and the corresponding operational AIRS products (AIRS_L2) as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) (AIRS_DR) are compared respectively with ECMWF T799 data. The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR. Furthermore, the analysis of the typical GW events induced by the mountain Andes and the typhoon "Soulik" using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.

Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears.

Diagnosis of malaria in endemic areas is hampered by the lack of a rapid, stain-free and sensitive method to directly identify parasites in peripheral blood. Herein, we report the use of Fourier ptychography to generate wide-field high-resolution quantitative phase images of erythrocytes infected with malaria parasites, from a whole blood sample. We are able to image thousands of erythrocytes (red blood cells) in a single field of view and make a determination of infection status of the quantitative phase image of each segmented cell based on machine learning (random forest) and deep learning (VGG16) models. Our random forest model makes use of morphology and texture based features of the quantitative phase images. In order to label the quantitative images of the cells as either infected or uninfected before training the models, we make use of a Plasmodium berghei strain expressing GFP (green fluorescent protein) in all life cycle stages. By overlaying the fluorescence image with the quantitative phase image we could identify the infected subpopulation of erythrocytes for labelling purposes. Our machine learning model (random forest) achieved 91% specificity and 72% sensitivity while our deep learning model (VGG16) achieved 98% specificity and 57% sensitivity. These results highlight the potential for quantitative phase imaging coupled with artificial intelligence to develop an easy to use platform for the rapid and sensitive diagnosis of malaria.

A PIV-Based Algorithm for Simultaneous Determination of Multiple Velocity Fields from Stratified Crossflows in Single Field of View

This study presents a new imaging-based algorithm for simultaneously determining multiple velocity fields from stratified crossflows optically captured in a single field of view. The concept implements an additional automatic peak finding scheme into the conventional particle image velocimetry (PIV) analysis procedure, identifying multiple prominent peak cross-correlation coefficients corresponding to the flows in various directions. To examine the validity, synthetic particle images generated by computer visions and image data acquired by PIV measurements are employed in the validation study. With both root-mean-square errors (RMSEs) in magnitude and direction being found to be temporally random, the validation results suggest that the performance of the new algorithm is ideal for steady or quasi-steady flows. This implies that the new algorithm may also work well for the flows repeatable with identical initial and boundary conditions. For transient flows, more valuable data can be obtained with the new algorithm, particularly in large-scale experiments or field measurements. Moreover, tests on synthetic images show that the RMSE in magnitude decays exponentially with increasing tracking particle density, and a density of 30% is found to be the lowest for the minimum RMSE in magnitude. Discussions on the error reduction, limitations of the new algorithm, suggestions for applications, and guidance on spurious vector removal are given as well.

Scheimpflug Camera-Based Technique for Multi-Point Displacement Monitoring of Bridges.

Owing to the limited field of view (FOV) and depth of field (DOF) of a conventional camera, it is quite difficult to employ a single conventional camera to simultaneously measure high-precision displacements at many points on a bridge of dozens or hundreds of meters. Researchers have attempted to obtain a large FOV and wide DOF by a multi-camera system; however, with the growth of the camera number, the cost, complexity and instability of multi-camera systems will increase exponentially. This study proposes a multi-point displacement measurement method for bridges based on a low-cost Scheimpflug camera. The Scheimpflug camera, which meets the Scheimpflug condition, can enlarge the depth of field of the camera without reducing the lens aperture and magnification; thus, when the measurement points are aligned in the depth direction, all points can be clearly observed in a single field of view with a high-power zoom lens. To reduce the impact of camera motions, a motion compensation method applied to the Scheimpflug camera is proposed according to the characteristic that the image plane is not perpendicular to the lens axis in the Scheimpflug camera. Several tests were conducted for performance verification under diverse settings. The results showed that the motion errors in x and y directions were reduced by at least 62% and 92%, respectively, using the proposed method, and the measurements of the camera were highly consistent with LiDAR-based measurements.

Satellite observation of stratospheric intrusions and ozone transport using CrIS on SNPP

The Single Field of View (SFOV) ozone (O3) profiles derived from the Cross-track Infrared Sounder (CrIS) onboard Suomi National Polar-Orbiting Partnership (SNPP), with a high horizontal resolution of approximately 14 km at nadir and good sensitivity to O3 in the upper troposphere and lower stratosphere (UTLS), provide a prominent opportunity to examine stratosphere to troposphere (STT) transport. A process-oriented analysis was performed to examine the fine-scale features of a stratospheric intrusion (SI) event on June 11–13, 2017 in the southwestern US using the CrIS SFOV products together with wind and potential velocity (PV) from models. It was found that the location and strength of O3 enhancement correlate well with the PV contours, and the intrusion depth can be characterized using the vertical cross-sections of O3 and relative humidity (RH). In addition, the capability to use total column ozone (TCO) to identify SI events was confirmed through analysis of TCO from CrIS SFOV and other satellite and reanalysis products. The ozone/PV ratio was derived using SFOV O3 and model PV, and the values, ranging from 23.2 to 35.8 ppbv PVU−1 (1 PV unit (PVU) = 10−6 km2 kg−1 s−1), are in the lower end of previous estimations. These results demonstrate the advantages of the SFOV products in monitoring the fine-scale ozone transport and thermodynamic structure of SI events, as well as their potential value for weather and climate study.

Mechanical Particle Filter-Based Active Vision System for Fast Wide-Area Multiobject Detection

Sliding window searching is a common paradigm employed for large Field-of-View (FOV) multi-object detection. However, low target resolution and irregular distribution of objects make the traditional method inefficient and hard to meet the demands of fast object detection in a wide area. To address the above issues, in this paper we design an ultrafast switching galvano-mirror based active vision system that can simultaneously search and detect multi-object at 300 frames per second (fps) in a large FOV, without the guidance of a high-resolution (HR) panoramic camera. Specifically, a novel mechanical particle filter (MPF) framework is proposed to improve the efficiency of searching potential objects by constructing the probability distribution model and then iteratively locating target objects, where each particle is represented by an image of a single small FOV that is controlled and captured by a switching galvano-mirror. The object detector is used to determine if the particle image contains target objects. Then, to reduce the total scanning cost for capturing all particles, we introduce a center partitioned scanning algorithm that significantly speeds up the scanning process by 10× or more. Moreover, we optimize the processing pipeline for low latency sensing by using a high-parallelism visual feedback scheme to improve efficiency. Finally, an efficient and fast active vision system is implemented, which is based on a 2-axis galvano-mirror and a high-speed camera without multi-camera fusion for active object searching. Abundant experimental results demonstrate the effectiveness of our proposed system in indoor environments and real-world scenarios. We also verify the performance in terms of scanning speed and detection accuracy.

Efficient misalignment calculation for two-mirror optical systems based on wavefront aberrations in a single field of view

Computer-aided alignment (CAA) is a key technology for improving the assembly efficiency and performance of optical systems. However, because of the coma-free pivot point in two-mirror optical systems (TMOSs), the wavefront aberrations from multiple fields of view (FoVs) are required to compute misalignments, and this is not practical. Therefore, misalignment-caused wavefront aberrations were investigated based on the nodal aberration theory, and an analytical aberration computation model for TMOSs was derived. Based on this, an efficient misalignment calculation method based on single-FoV aberrations that introduces known misalignments was proposed and tested by conducting Monte Carlo simulations using the Hilbert telescope. The results showed that micron-level precision can be achieved for the aberration and misalignment calculation without any measurement noise and that a greater number of introduced misalignments can decrease the effect of measurement noise, thereby verifying the effectiveness of the proposed model and method. The findings of this research will open a new direction in CAA by introducing the misalignment rather than the FoV.

A Novel Microfluidic Device for the Neutrophil Functional Phenotype Analysis: Effects of Glucose and Its Derivatives AGEs.

Neutrophil dysfunction is closely related to the pathophysiology of patients with diabetes mellitus, but existing immunoassays are difficult to implement in clinical applications, and neutrophil’s chemotaxis as a functional biomarker for diabetes mellitus prognostic remains largely unexplored. Herein, a novel microfluidic device consisted of four independent test units with four cell docking structures was developed to study the neutrophil chemotaxis, which allowed multiple cell migration observations under a single field of view (FOV) and guaranteed more reliable results. In vitro studies, the chemotaxis of healthy neutrophils to N-Formyl-Met-Leu-Phe (fMLP) gradient (0, 10, 100, and 1000 nM) was concentration-dependent. The distinct promotion or suppression in the chemotaxis of metformin or pravastatin pretreated cells were observed after exposure to 100 nM fMLP gradient, indicating the feasibility and efficiency of this novel microfluidic device for clinically relevant evaluation of neutrophil functional phenotype. Further, the chemotaxis of neutrophils pretreated with 25, 50, or 70 mM of glucose was quantitatively lower than that of the control groups (i.e., 5 mM normal serum level). Neutrophils exposed to highly concentrated advanced glycation end products (AGEs) (0.2, 0.5, or 1.0 μM; 0.13 μM normal serum AGEs level), a product of prolonged hyperglycemia, showed that the higher the AGEs concentration was, the weaker the migration speed became. Specifically, neutrophils exposed to high concentrations of glucose or AGEs also showed a stronger drifting along with the flow, further demonstrating the change of neutrophil chemotaxis. Interestingly, adding the N-benzyl-4-chloro-N-cyclohexylbenzamide (FPS-ZM1) (i.e., high-affinity RAGE inhibitor) into the migration medium with AGEs could hinder the binding between AGEs and AGE receptor (RAGE) located on the neutrophil, thereby keeping the normal chemotaxis of neutrophils than the ones incubated with AGEs alone. These results revealed the negative effects of high concentrations of glucose and AGEs on the neutrophil chemotaxis, suggesting that patients with diabetes should manage serum AGEs and also pay attention to blood glucose indexes. Overall, this novel microfluidic device could significantly characterize the chemotaxis of neutrophils and have the potential to be further improved into a tool for risk stratification of diabetes mellitus.

Estimation of renal perfusion based on measurement of rubidium-82 clearance by PET/CT scanning in healthy subjects

BackgroundChanges in renal blood flow (RBF) may play a pathophysiological role in hypertension and kidney disease. However, RBF determination in humans has proven difficult. We aimed to confirm the feasibility of RBF estimation based on positron emission tomography/computed tomography (PET/CT) and rubidium-82 (82Rb) using the abdominal aorta as input function in a 1-tissue compartment model.MethodsEighteen healthy subjects underwent two dynamic 82Rb PET/CT scans in two different fields of view (FOV). FOV-A included the left ventricular blood pool (LVBP), the abdominal aorta (AA) and the majority of the kidneys. FOV-B included AA and the kidneys in their entirety. In FOV-A, an input function was derived from LVBP and from AA, in FOV-B from AA. One-tissue compartmental modelling was performed using tissue time activity curves generated from volumes of interest (VOI) contouring the kidneys, where the renal clearance of 82Rb is represented by the K1 kinetic parameter. Total clearance for both kidneys was calculated by multiplying the K1 values with the volume of VOIs used for analysis. Intra-assay coefficients of variation and inter-observer variation were calculated.ResultsFor both kidneys, K1 values derived from AA did not differ significantly from values obtained from LVBP, neither were significant differences seen between AA in FOV-A and AA in FOV-B, nor between the right and left kidneys. For both kidneys, the intra-assay coefficients of variation were low (~ 5%) for both input functions. The measured K1 of 2.80 ml/min/cm3 translates to a total clearance for both kidneys of 766 ml/min/1.73 m2.ConclusionMeasurement of renal perfusion based on PET/CT and 82Rb using AA as input function in a 1-tissue compartment model is feasible in a single FOV. Based on previous studies showing 82Rb to be primarily present in plasma, the measured K1 clearance values are most likely representative of effective renal plasma flow (ERPF) rather than estimated RBF values, but as the accurate calculation of total clearance/flow is very much dependent on the analysed volume, a standardised definition for the employed renal volumes is needed to allow for proper comparison with standard ERPF and RBF reference methods.

CalTrack: High-Throughput Automated Calcium Transient Analysis in Cardiomyocytes.

[Figure: see text].

Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels

Reflectance confocal microscopy (RCM) is a non-invasive imaging tool that reduces the need for invasive histopathology for skin cancer diagnoses by providing high-resolution mosaics showing the architectural patterns of skin, which are used to identify malignancies in-vivo. RCM mosaics are similar to dermatopathology sections, both requiring extensive training to interpret. However, these modalities differ in orientation, as RCM mosaics are horizontal (parallel to the skin surface) while histopathology sections are vertical, and contrast mechanism, RCM with a single (reflectance) mechanism resulting in grayscale images and histopathology with multi-factor color-stained contrast. Image analysis and machine learning methods can potentially provide a diagnostic aid to clinicians to interpret RCM mosaics, eventually helping to ease the adoption and more efficiently utilizing RCM in routine clinical practice. However standard supervised machine learning may require a prohibitive volume of hand-labeled training data. In this paper, we present a weakly supervised machine learning model to perform semantic segmentation of architectural patterns encountered in RCM mosaics. Unlike more widely used fully supervised segmentation models that require pixel-level annotations, which are very labor-demanding and error-prone to obtain, here we focus on training models using only patch-level labels (e.g. a single field of view within an entire mosaic). We segment RCM mosaics into “benign” and “aspecific (nonspecific)” regions, where aspecific regions represent the loss of regular architecture due to injury and/or inflammation, pre-malignancy, or malignancy. We adopt Efficientnet, a deep neural network (DNN) proven to accurately accomplish classification tasks, to generate class activation maps, and use a Gaussian weighting kernel to stitch smaller images back into larger fields of view. The trained DNN achieved an average area under the curve of 0.969, and Dice coefficient of 0.778 showing the feasibility of spatial localization of aspecific regions in RCM images, and making the diagnostics decision model more interpretable to the clinicians.

Speeded-Up Robust Features-based image mosaic method for large-scale microscopic hyperspectral pathological imaging

Microscopic hyperspectral imaging technology has been widely used in pathological analysis as it can obtain both spatial and spectral information of samples. However, most hyperspectral imaging systems can only capture images in a single field of view. Therefore, an image mosaic is one of the most important steps in a large-scale microscopic hyperspectral imaging system. This paper proposes a microscopic hyperspectral image (HSI) mosaic method based on Speeded-Up Robust Features (SURF) and linear synthesis to achieve large-scale HSIs. In contrast to other SURF-based image mosaic methods, the proposed method leverages both image content and coordinate information to improve the accuracy and stability of the image mosaic. In addition, multiple bands of HSIs with different texture information and grayscale are applied in image matching to take full advantage of spatial redundancy. Simultaneously, a blank microscopic HSI screening method is introduced in this paper to pick out a clearer blank image for better preprocessing, i.e. removing interference in the optical path and the interference of dust on slides. Finally, the preprocessed images are synthesized by linear-based synthesis methods due to their simple synthesis structure and better universality. Additionally, a file format, i.e. hyperslide, is defined for large-scale HSIs and can be browsed with HyperViewer software. Experimental results show that the proposed microscopic HSI mosaic method can obtain high-quality large-scale microscopic HSIs of tissue sections.

Single full-FOV reconstruction Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique that has high-resolution and wide field-of-view (FOV). FPM bypasses the NA limit of the system by stitching a number of variable-illuminated measured images in Fourier space. On the basis of the wide FOV of the low NA objective, the high-resolution image with a wide FOV can be reconstructed through the phase recovery algorithm. However, the high-resolution reconstruction images are affected by the LED array point light source. The results are: (1) the intensities collected by the sample are severely declined when edge LEDs illuminate the sample; (2) the multiple reconstructions are caused by wavevectors inconsistency for the full FOV images. Here, we propose a new lighting scheme termed full FOV Fourier ptychographic microscopy (F3PM). By combining the LED array and telecentric lens, the method can provide plane waves with different angles while maintaining uniform intensity. Benefiting from the telecentric performance and f‒θ property of the telecentric lens, the system stability is improved and the relationship between the position of LED and its illumination angle is simplified. The excellent plane wave provided by the telecentric lens guarantees the same wavevector in the full FOV, and we use this wavevector to reconstruct the full FOV during one time. The area and diameter of the single reconstruction FOV reached 14.6mm 2 and 5.4 mm, respectively, and the diameter is very close to the field number (5.5 mm) of the 4× objective. Compared with the traditional FPM, we have increased the diameter of FOV in a single reconstruction by ∼ 10 times, eliminating the complicated steps of computational redundancy and image stitching.